{"cells":[{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":["import pandas as pd\n",""]},{"cell_type":"markdown","metadata":{},"source":["## 请补全代码（10分）导入数据"]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[{"output_type":"execute_result","data":{"text/plain":["     Channel  Region  Fresh   Milk  Grocery  Frozen  Detergents_Paper  \\\n","0          2       3  12669   9656     7561     214              2674   \n","1          2       3   7057   9810     9568    1762              3293   \n","2          2       3   6353   8808     7684    2405              3516   \n","3          1       3  13265   1196     4221    6404               507   \n","4          2       3  22615   5410     7198    3915              1777   \n","..       ...     ...    ...    ...      ...     ...               ...   \n","435        1       3  29703  12051    16027   13135               182   \n","436        1       3  39228   1431      764    4510                93   \n","437        2       3  14531  15488    30243     437             14841   \n","438        1       3  10290   1981     2232    1038               168   \n","439        1       3   2787   1698     2510      65               477   \n","\n","     Delicassen  \n","0          1338  \n","1          1776  \n","2          7844  \n","3          1788  \n","4          5185  \n","..          ...  \n","435        2204  \n","436        2346  \n","437        1867  \n","438        2125  \n","439          52  \n","\n","[440 rows x 8 columns]"],"text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Channel</th>\n      <th>Region</th>\n      <th>Fresh</th>\n      <th>Milk</th>\n      <th>Grocery</th>\n      <th>Frozen</th>\n      <th>Detergents_Paper</th>\n      <th>Delicassen</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2</td>\n      <td>3</td>\n      <td>12669</td>\n      <td>9656</td>\n      <td>7561</td>\n      <td>214</td>\n      <td>2674</td>\n      <td>1338</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>3</td>\n      <td>7057</td>\n      <td>9810</td>\n      <td>9568</td>\n      <td>1762</td>\n      <td>3293</td>\n      <td>1776</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>3</td>\n      <td>6353</td>\n      <td>8808</td>\n      <td>7684</td>\n      <td>2405</td>\n      <td>3516</td>\n      <td>7844</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>3</td>\n      <td>13265</td>\n      <td>1196</td>\n      <td>4221</td>\n      <td>6404</td>\n      <td>507</td>\n      <td>1788</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2</td>\n      <td>3</td>\n      <td>22615</td>\n      <td>5410</td>\n      <td>7198</td>\n      <td>3915</td>\n      <td>1777</td>\n      <td>5185</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>435</th>\n      <td>1</td>\n      <td>3</td>\n      <td>29703</td>\n      <td>12051</td>\n      <td>16027</td>\n      <td>13135</td>\n      <td>182</td>\n      <td>2204</td>\n    </tr>\n    <tr>\n      <th>436</th>\n      <td>1</td>\n      <td>3</td>\n      <td>39228</td>\n      <td>1431</td>\n      <td>764</td>\n      <td>4510</td>\n      <td>93</td>\n      <td>2346</td>\n    </tr>\n    <tr>\n      <th>437</th>\n      <td>2</td>\n      <td>3</td>\n      <td>14531</td>\n      <td>15488</td>\n      <td>30243</td>\n      <td>437</td>\n      <td>14841</td>\n      <td>1867</td>\n    </tr>\n    <tr>\n      <th>438</th>\n      <td>1</td>\n      <td>3</td>\n      <td>10290</td>\n      <td>1981</td>\n      <td>2232</td>\n      <td>1038</td>\n      <td>168</td>\n      <td>2125</td>\n    </tr>\n    <tr>\n      <th>439</th>\n      <td>1</td>\n      <td>3</td>\n      <td>2787</td>\n      <td>1698</td>\n      <td>2510</td>\n      <td>65</td>\n      <td>477</td>\n      <td>52</td>\n    </tr>\n  </tbody>\n</table>\n<p>440 rows × 8 columns</p>\n</div>"},"metadata":{},"execution_count":2}],"source":["df = pd.read_csv(\"Wholesale customers data.csv\")\n","df"]},{"cell_type":"markdown","metadata":{},"source":["### 请补全代码（5分）\n","查看数据缺失值---数据并没有缺失值，数据完整性比较好\n","查询结果中不存在缺失值"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 440 entries, 0 to 439\nData columns (total 8 columns):\n #   Column            Non-Null Count  Dtype\n---  ------            --------------  -----\n 0   Channel           440 non-null    int64\n 1   Region            440 non-null    int64\n 2   Fresh             440 non-null    int64\n 3   Milk              440 non-null    int64\n 4   Grocery           440 non-null    int64\n 5   Frozen            440 non-null    int64\n 6   Detergents_Paper  440 non-null    int64\n 7   Delicassen        440 non-null    int64\ndtypes: int64(8)\nmemory usage: 27.6 KB\n"]}],"source":["df.info()\n"]},{"cell_type":"markdown","metadata":{},"source":["### 请补全代码（5分）\n","数据标准化"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[{"output_type":"execute_result","data":{"text/plain":["          Channel      Region          Fresh          Milk       Grocery  \\\n","count  440.000000  440.000000     440.000000    440.000000    440.000000   \n","mean     1.322727    2.543182   12000.297727   5796.265909   7951.277273   \n","std      0.468052    0.774272   12647.328865   7380.377175   9503.162829   \n","min      1.000000    1.000000       3.000000     55.000000      3.000000   \n","25%      1.000000    2.000000    3127.750000   1533.000000   2153.000000   \n","50%      1.000000    3.000000    8504.000000   3627.000000   4755.500000   \n","75%      2.000000    3.000000   16933.750000   7190.250000  10655.750000   \n","max      2.000000    3.000000  112151.000000  73498.000000  92780.000000   \n","\n","             Frozen  Detergents_Paper    Delicassen  \n","count    440.000000        440.000000    440.000000  \n","mean    3071.931818       2881.493182   1524.870455  \n","std     4854.673333       4767.854448   2820.105937  \n","min       25.000000          3.000000      3.000000  \n","25%      742.250000        256.750000    408.250000  \n","50%     1526.000000        816.500000    965.500000  \n","75%     3554.250000       3922.000000   1820.250000  \n","max    60869.000000      40827.000000  47943.000000  "],"text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Channel</th>\n      <th>Region</th>\n      <th>Fresh</th>\n      <th>Milk</th>\n      <th>Grocery</th>\n      <th>Frozen</th>\n      <th>Detergents_Paper</th>\n      <th>Delicassen</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>440.000000</td>\n      <td>440.000000</td>\n      <td>440.000000</td>\n      <td>440.000000</td>\n      <td>440.000000</td>\n      <td>440.000000</td>\n      <td>440.000000</td>\n      <td>440.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>1.322727</td>\n      <td>2.543182</td>\n      <td>12000.297727</td>\n      <td>5796.265909</td>\n      <td>7951.277273</td>\n      <td>3071.931818</td>\n      <td>2881.493182</td>\n      <td>1524.870455</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.468052</td>\n      <td>0.774272</td>\n      <td>12647.328865</td>\n      <td>7380.377175</td>\n      <td>9503.162829</td>\n      <td>4854.673333</td>\n      <td>4767.854448</td>\n      <td>2820.105937</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>55.000000</td>\n      <td>3.000000</td>\n      <td>25.000000</td>\n      <td>3.000000</td>\n      <td>3.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>1.000000</td>\n      <td>2.000000</td>\n      <td>3127.750000</td>\n      <td>1533.000000</td>\n      <td>2153.000000</td>\n      <td>742.250000</td>\n      <td>256.750000</td>\n      <td>408.250000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>8504.000000</td>\n      <td>3627.000000</td>\n      <td>4755.500000</td>\n      <td>1526.000000</td>\n      <td>816.500000</td>\n      <td>965.500000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>2.000000</td>\n      <td>3.000000</td>\n      <td>16933.750000</td>\n      <td>7190.250000</td>\n      <td>10655.750000</td>\n      <td>3554.250000</td>\n      <td>3922.000000</td>\n      <td>1820.250000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>2.000000</td>\n      <td>3.000000</td>\n      <td>112151.000000</td>\n      <td>73498.000000</td>\n      <td>92780.000000</td>\n      <td>60869.000000</td>\n      <td>40827.000000</td>\n      <td>47943.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{},"execution_count":4}],"source":["df.describe()"]},{"cell_type":"markdown","metadata":{},"source":["#在此使用Normalizer---使用方法同MinMaxScaler、StandardScaler\n","#请补全代码（10分）"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[],"source":["from sklearn.preprocessing import MinMaxScaler, Normalizer, StandardScaler\n",""]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[{"output_type":"execute_result","data":{"text/plain":["        Fresh      Milk   Grocery    Frozen  Detergents_Paper  Delicassen\n","0    0.708333  0.539874  0.422741  0.011965          0.149505    0.074809\n","1    0.442198  0.614704  0.599540  0.110409          0.206342    0.111286\n","2    0.396552  0.549792  0.479632  0.150119          0.219467    0.489619\n","3    0.856837  0.077254  0.272650  0.413659          0.032749    0.115494\n","4    0.895416  0.214203  0.284997  0.155010          0.070358    0.205294\n","..        ...       ...       ...       ...               ...         ...\n","435  0.776890  0.315197  0.419191  0.343549          0.004760    0.057646\n","436  0.990872  0.036146  0.019298  0.113919          0.002349    0.059258\n","437  0.364462  0.388465  0.758545  0.010961          0.372237    0.046827\n","438  0.937737  0.180530  0.203404  0.094594          0.015310    0.193653\n","439  0.672296  0.409601  0.605477  0.015680          0.115065    0.012544\n","\n","[440 rows x 6 columns]"],"text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Fresh</th>\n      <th>Milk</th>\n      <th>Grocery</th>\n      <th>Frozen</th>\n      <th>Detergents_Paper</th>\n      <th>Delicassen</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.708333</td>\n      <td>0.539874</td>\n      <td>0.422741</td>\n      <td>0.011965</td>\n      <td>0.149505</td>\n      <td>0.074809</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.442198</td>\n      <td>0.614704</td>\n      <td>0.599540</td>\n      <td>0.110409</td>\n      <td>0.206342</td>\n      <td>0.111286</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.396552</td>\n      <td>0.549792</td>\n      <td>0.479632</td>\n      <td>0.150119</td>\n      <td>0.219467</td>\n      <td>0.489619</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.856837</td>\n      <td>0.077254</td>\n      <td>0.272650</td>\n      <td>0.413659</td>\n      <td>0.032749</td>\n      <td>0.115494</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.895416</td>\n      <td>0.214203</td>\n      <td>0.284997</td>\n      <td>0.155010</td>\n      <td>0.070358</td>\n      <td>0.205294</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>435</th>\n      <td>0.776890</td>\n      <td>0.315197</td>\n      <td>0.419191</td>\n      <td>0.343549</td>\n      <td>0.004760</td>\n      <td>0.057646</td>\n    </tr>\n    <tr>\n      <th>436</th>\n      <td>0.990872</td>\n      <td>0.036146</td>\n      <td>0.019298</td>\n      <td>0.113919</td>\n      <td>0.002349</td>\n      <td>0.059258</td>\n    </tr>\n    <tr>\n      <th>437</th>\n      <td>0.364462</td>\n      <td>0.388465</td>\n      <td>0.758545</td>\n      <td>0.010961</td>\n      <td>0.372237</td>\n      <td>0.046827</td>\n    </tr>\n    <tr>\n      <th>438</th>\n      <td>0.937737</td>\n      <td>0.180530</td>\n      <td>0.203404</td>\n      <td>0.094594</td>\n      <td>0.015310</td>\n      <td>0.193653</td>\n    </tr>\n    <tr>\n      <th>439</th>\n      <td>0.672296</td>\n      <td>0.409601</td>\n      <td>0.605477</td>\n      <td>0.015680</td>\n      <td>0.115065</td>\n      <td>0.012544</td>\n    </tr>\n  </tbody>\n</table>\n<p>440 rows × 6 columns</p>\n</div>"},"metadata":{},"execution_count":6}],"source":["df_ = pd.DataFrame(\n","    Normalizer(norm=\"l2\").fit_transform(df.iloc[:, 2:]), columns=df.columns[2:]\n",")\n","df_.describe()\n","df_"]},{"cell_type":"markdown","metadata":{},"source":["适用轮廓系数找到最优K\n","#请补全代码（25分）"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[],"source":["from sklearn.cluster import KMeans\n","from sklearn.metrics import silhouette_score  # 轮廓系数\n","import matplotlib.pyplot as plt\n",""]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(0, 0.5002248259665941, 'score')"]},"metadata":{},"execution_count":8},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/svg+xml":"<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"248.518125pt\" version=\"1.1\" viewBox=\"0 0 378.465625 248.518125\" width=\"378.465625pt\" 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= []\n","for i in range(2, 7):\n","    KM = KMeans(n_clusters=i, random_state=0).fit(df_)\n","    score.append(silhouette_score(df_, KM.labels_))\n","\n","plt.plot(range(2, 7), score)\n","score.index(max(score)), max(score), \"score\""]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[{"output_type":"execute_result","data":{"text/plain":["           Fresh      Milk   Grocery    Frozen  Detergents_Paper  Delicassen\n","labels                                                                      \n","0       0.575088  0.233245  0.279252  0.625425          0.053748    0.109052\n","1       0.902462  0.179160  0.229113  0.149695          0.049047    0.074455\n","2       0.254118  0.486061  0.684665  0.095721          0.271384    0.097196"],"text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Fresh</th>\n      <th>Milk</th>\n      <th>Grocery</th>\n      <th>Frozen</th>\n      <th>Detergents_Paper</th>\n      <th>Delicassen</th>\n    </tr>\n    <tr>\n      <th>labels</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.575088</td>\n      <td>0.233245</td>\n      <td>0.279252</td>\n      <td>0.625425</td>\n      <td>0.053748</td>\n      <td>0.109052</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.902462</td>\n      <td>0.179160</td>\n      <td>0.229113</td>\n      <td>0.149695</td>\n      <td>0.049047</td>\n      <td>0.074455</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.254118</td>\n      <td>0.486061</td>\n      <td>0.684665</td>\n      <td>0.095721</td>\n      <td>0.271384</td>\n      <td>0.097196</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{},"execution_count":9}],"source":["KM = KMeans(n_clusters=3, random_state=0).fit(df_)\n","df_['labels'] = KM.labels_\n","df_.groupby(by='labels').mean()\n","\n",""]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.legend.Legend at 0x23d899cf520>"]},"metadata":{},"execution_count":10},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/svg+xml":"<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"231.84pt\" version=\"1.1\" viewBox=\"0 0 231.84 231.84\" width=\"231.84pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n  <rdf:RDF 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\n"},"metadata":{"needs_background":"light"}}],"source":["df_.groupby(by='labels').mean().plot.bar(figsize=(12,5))"]}],"nbformat":4,"nbformat_minor":2,"metadata":{"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":3},"orig_nbformat":4}}